Minor_Project.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install ipyleaflet"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZH2kcIb1-KfO",
        "outputId": "ca9f151e-b15f-42f0-d43f-804ae7f56e8b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting ipyleaflet\n",
            "  Downloading ipyleaflet-0.17.2-py3-none-any.whl (3.7 MB)\n",
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            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<9,>=7.6.0->ipyleaflet) (0.5.1)\n",
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            "Installing collected packages: xyzservices, traittypes, jedi, ipyleaflet\n",
            "Successfully installed ipyleaflet-0.17.2 jedi-0.18.2 traittypes-0.2.1 xyzservices-2023.2.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pystac"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h8rvQl8v-U6N",
        "outputId": "ee20e1cc-0760-4a54-c492-656447f6ef23"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting pystac\n",
            "  Downloading pystac-1.7.3-py3-none-any.whl (150 kB)\n",
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            "\u001b[?25hRequirement already satisfied: python-dateutil>=2.7.0 in /usr/local/lib/python3.10/dist-packages (from pystac) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7.0->pystac) (1.16.0)\n",
            "Installing collected packages: pystac\n",
            "Successfully installed pystac-1.7.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pystac_client"
      ],
      "metadata": {
        "id": "eWUeMEql-cuj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "57e8ba34-bd8c-49be-8eb3-f39a1cc8d18b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting pystac_client\n",
            "  Downloading pystac_client-0.6.1-py3-none-any.whl (30 kB)\n",
            "Requirement already satisfied: pystac>=1.7.0 in /usr/local/lib/python3.10/dist-packages (from pystac_client) (1.7.3)\n",
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            "Installing collected packages: pystac_client\n",
            "Successfully installed pystac_client-0.6.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install odc-stac"
      ],
      "metadata": {
        "id": "0VGTKRxx-jNM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a17d6425-d237-4be6-8bfa-b0b8736f2d03"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting odc-stac\n",
            "  Downloading odc_stac-0.3.5-py3-none-any.whl (55 kB)\n",
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            "Collecting affine\n",
            "  Downloading affine-2.4.0-py3-none-any.whl (15 kB)\n",
            "Collecting rasterio!=1.3.0,!=1.3.1,>=1.0.0\n",
            "  Downloading rasterio-1.3.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (20.0 MB)\n",
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            "Collecting odc-geo>=0.3.0\n",
            "  Downloading odc_geo-0.3.3-py3-none-any.whl (114 kB)\n",
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            "Collecting pyproj>=3.0.0\n",
            "  Downloading pyproj-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.7 MB)\n",
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            "Requirement already satisfied: attrs in /usr/local/lib/python3.10/dist-packages (from rasterio!=1.3.0,!=1.3.1,>=1.0.0->odc-stac) (23.1.0)\n",
            "Collecting snuggs>=1.4.1\n",
            "  Downloading snuggs-1.4.7-py3-none-any.whl (5.4 kB)\n",
            "Collecting click-plugins\n",
            "  Downloading click_plugins-1.1.1-py2.py3-none-any.whl (7.5 kB)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from rasterio!=1.3.0,!=1.3.1,>=1.0.0->odc-stac) (67.7.2)\n",
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            "Collecting cligj>=0.5\n",
            "  Downloading cligj-0.7.2-py3-none-any.whl (7.1 kB)\n",
            "Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.10/dist-packages (from xarray>=0.19->odc-stac) (23.1)\n",
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            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7.0->pystac<2,>=1.0.0->odc-stac) (1.16.0)\n",
            "Requirement already satisfied: pyparsing>=2.1.6 in /usr/local/lib/python3.10/dist-packages (from snuggs>=1.4.1->rasterio!=1.3.0,!=1.3.1,>=1.0.0->odc-stac) (3.0.9)\n",
            "Installing collected packages: snuggs, pyproj, cligj, click-plugins, affine, rasterio, odc-geo, odc-stac\n",
            "Successfully installed affine-2.4.0 click-plugins-1.1.1 cligj-0.7.2 odc-geo-0.3.3 odc-stac-0.3.5 pyproj-3.5.0 rasterio-1.3.6 snuggs-1.4.7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7yIQIBCFBMHQ",
        "outputId": "5aba6c90-b629-4ac1-99c9-fcb518b158b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Cloning the repository for leafmap"
      ],
      "metadata": {
        "id": "421WBn-6DIAE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/giswqs/leafmap.git"
      ],
      "metadata": {
        "id": "X-UopffBDGyw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1aad7ada-c823-4d6a-9a0a-aaac4d38716a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'leafmap'...\n",
            "remote: Enumerating objects: 9476, done.\u001b[K\n",
            "remote: Counting objects: 100% (1707/1707), done.\u001b[K\n",
            "remote: Compressing objects: 100% (798/798), done.\u001b[K\n",
            "remote: Total 9476 (delta 926), reused 1678 (delta 901), pack-reused 7769\u001b[K\n",
            "Receiving objects: 100% (9476/9476), 284.86 MiB | 16.21 MiB/s, done.\n",
            "Resolving deltas: 100% (6173/6173), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install planetary-computer"
      ],
      "metadata": {
        "id": "_WAUYUf3tqyz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "21afe8a0-2028-48c1-e3f8-cc8cbcdb5cc9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting planetary-computer\n",
            "  Downloading planetary_computer-0.5.1-py3-none-any.whl (17 kB)\n",
            "Requirement already satisfied: pydantic[dotenv]>=1.7.3 in /usr/local/lib/python3.10/dist-packages (from planetary-computer) (1.10.7)\n",
            "Requirement already satisfied: pystac>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from planetary-computer) (1.7.3)\n",
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            "Requirement already satisfied: pytz>=2020.5 in /usr/local/lib/python3.10/dist-packages (from planetary-computer) (2022.7.1)\n",
            "Requirement already satisfied: click>=7.1 in /usr/local/lib/python3.10/dist-packages (from planetary-computer) (8.1.3)\n",
            "Requirement already satisfied: requests>=2.25.1 in /usr/local/lib/python3.10/dist-packages (from planetary-computer) (2.27.1)\n",
            "Requirement already satisfied: typing-extensions>=4.2.0 in /usr/local/lib/python3.10/dist-packages (from pydantic[dotenv]>=1.7.3->planetary-computer) (4.5.0)\n",
            "Collecting python-dotenv>=0.10.4\n",
            "  Downloading python_dotenv-1.0.0-py3-none-any.whl (19 kB)\n",
            "Requirement already satisfied: python-dateutil>=2.7.0 in /usr/local/lib/python3.10/dist-packages (from pystac>=1.0.0->planetary-computer) (2.8.2)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.25.1->planetary-computer) (1.26.15)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.25.1->planetary-computer) (3.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.25.1->planetary-computer) (2022.12.7)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests>=2.25.1->planetary-computer) (2.0.12)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7.0->pystac>=1.0.0->planetary-computer) (1.16.0)\n",
            "Installing collected packages: python-dotenv, planetary-computer\n",
            "Successfully installed planetary-computer-0.5.1 python-dotenv-1.0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!planetarycomputer configure"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HhodUTRTDGvU",
        "outputId": "6a044e9f-a4f8-4da8-ec53-11913351a62b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Please enter your API subscription key: 8037994b42c1450e9a4daca46c196464\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import planetary_computer as pc\n",
        "# import pystac_client\n",
        "\n",
        "# from pystac_client import Client\n",
        "# import planetary_computer, requests\n",
        "# api = Client.open(\n",
        "#    'https://planetarycomputer.microsoft.com/api/stac/v1',\n",
        "#    modifier=planetary_computer.sign_inplace,\n",
        "# )"
      ],
      "metadata": {
        "id": "Voig8GZVu4BB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install Rich"
      ],
      "metadata": {
        "id": "GHYAHly-nwzP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f630e456-411e-4ddd-eb67-97e599dd3fc2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: Rich in /usr/local/lib/python3.10/dist-packages (13.3.4)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from Rich) (2.14.0)\n",
            "Requirement already satisfied: markdown-it-py<3.0.0,>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from Rich) (2.2.0)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py<3.0.0,>=2.2.0->Rich) (0.1.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python -m rich"
      ],
      "metadata": {
        "id": "dQoNSUqpn3to"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading the dependencies"
      ],
      "metadata": {
        "id": "9LYGuC9y-GWR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## We have to get access to planetary_computer API"
      ],
      "metadata": {
        "id": "c-Cd5RARK5yp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from odc import stac\n",
        "# Supress Warnings\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "# Visualization\n",
        "import ipyleaflet\n",
        "import matplotlib.pyplot as plt\n",
        "from IPython.display import Image\n",
        "import seaborn as sns\n",
        "\n",
        "# Data Science\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# Feature Engineering\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Machine Learning\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import f1_score, accuracy_score,classification_report,confusion_matrix\n",
        "\n",
        "# Planetary Computer Tools\n",
        "import pystac\n",
        "import pystac_client\n",
        "# import odc-stac\n",
        "from pystac_client import Client\n",
        "from pystac.extensions.eo import EOExtension as eo\n",
        "from odc.stac import stac_load\n",
        "import planetary_computer as pc\n",
        "pc.settings.set_subscription_key('8037994b42c1450e9a4daca46c196464')\n",
        "\n",
        "# Others\n",
        "import requests\n",
        "import rich.table"
      ],
      "metadata": {
        "id": "x7D9Lbl58paq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from itertools import cycle\n",
        "from tqdm import tqdm\n",
        "tqdm.pandas()"
      ],
      "metadata": {
        "id": "Q0pmPRgpIeEV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Curated the data from a certain region of Vietnam in 2020\n",
        "crop_presence_data = pd.read_csv(\"/content/drive/MyDrive/Crop_Location_Data_20221201.csv\")\n",
        "crop_presence_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "hQjB6lJ5-Rep",
        "outputId": "bca9e9be-3c4b-43e3-c084-c82588c1dcc1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     Latitude and Longitude Class of Land\n",
              "0   (10.323727047081501, 105.2516346045924)          Rice\n",
              "1  (10.322364360592521, 105.27843410554115)          Rice\n",
              "2  (10.321455902933202, 105.25254306225168)          Rice\n",
              "3  (10.324181275911162, 105.25118037576274)          Rice\n",
              "4  (10.324635504740822, 105.27389181724476)          Rice"
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          "metadata": {},
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      ]
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      "cell_type": "markdown",
      "source": [
        "Sentinel-1 radar data penetrates through the clouds, thus helping us to get the band values with minimal atmospheric attenuation. Band values such as VV and VH help us in distinguishing between the rice and non rice crops. Hence we are choosing VV and VH as predictor variables for this experiment.\n",
        "\n",
        "VV - gamma naught values of signal transmitted with vertical polarization and received with vertical polarization with radiometric terrain correction applied.\n",
        "\n",
        "VH - gamma naught values of signal transmitted with vertical polarization and received with horizontal polarization with radiometric terrain correction applied."
      ],
      "metadata": {
        "id": "CUyuwrOWFed-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install odc\n",
        "import odc"
      ],
      "metadata": {
        "id": "hNiQMehQXPs1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_sentinel_data(latlong,time_slice,assets):\n",
        "    '''\n",
        "    Returns VV and VH values for a given latitude and longitude\n",
        "    Attributes:\n",
        "    latlong - A tuple with 2 elements - latitude and longitude\n",
        "    time_slice - Timeframe for which the VV and VH values have to be extracted\n",
        "    assets - A list of bands to be extracted\n",
        "    '''\n",
        "    #latlong = (10.013942985253381, 105.67361318732796)\n",
        "    latlong=latlong.replace('(','').replace(')','').replace(' ','').split(',')\n",
        "    for i in range(2):\n",
        "      latlong[i] = float(latlong[i])\n",
        "\n",
        "\n",
        "    #bbox_of_interest = (float(latlong[1]) , float(latlong[0]), float(latlong[1]) , float(latlong[0]))\n",
        "    time_of_interest = time_slice\n",
        "    box_size_deg = 0.0004 # Surrounding box in degrees, yields approximately 5x5 pixel region\n",
        "\n",
        "    min_lon = latlong[1]-box_size_deg/2\n",
        "    min_lat = latlong[0]-box_size_deg/2\n",
        "    max_lon = latlong[1]+box_size_deg/2\n",
        "    max_lat = latlong[0]+box_size_deg/2\n",
        "\n",
        "    bbox_of_interest = (min_lon, min_lat, max_lon, max_lat)\n",
        "    ###\n",
        "\n",
        "    catalog = pystac_client.Client.open(\n",
        "        \"https://planetarycomputer.microsoft.com/api/stac/v1\"\n",
        "    )\n",
        "    search = catalog.search(\n",
        "        collections=[\"sentinel-1-rtc\"], bbox=bbox_of_interest, datetime=time_of_interest\n",
        "    )\n",
        "    items = list(search.get_all_items())\n",
        "    bands_of_interest = assests\n",
        "    resolution = 10  # meters per pixel\n",
        "    scale = resolution / 111320.0\n",
        "    data = stac_load([items[0]], bands = bands_of_interest, patch_url=pc.sign,crs=\"EPSG:4326\",resolution=scale,bbox=bbox_of_interest)\n",
        "    vh = data[\"vh\"].astype(\"float\").values.tolist()[0][0]\n",
        "    vv = data[\"vv\"].astype(\"float\").values.tolist()[0][0]\n",
        "\n",
        "    mean = data.mean(dim=['latitude','longitude']).compute()\n",
        "    dop = (mean.vv / (mean.vv + mean.vh))\n",
        "    m = 1 - dop\n",
        "    rvi = (np.sqrt(dop))*((4*mean.vh)/(mean.vv + mean.vh))\n",
        "    return vh[0],vv[0]"
      ],
      "metadata": {
        "id": "rhG59DZeFLWJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# xx = odc.stac.load(...crs=\"EPSG:3857\",resolution=10)\n",
        "# yy = odc.stac.load(...crs=\"EPSG:4326\",resolution=0.00009009)"
      ],
      "metadata": {
        "id": "VcJCJyjD2NIV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Sentinel-radiometric data\n",
        "# catalog = pystac_client.Client.open(\n",
        "#     \"https://planetarycomputer.microsoft.com/api/stac/v1\",\n",
        "#     modifier=pc.sign_inplace,\n",
        "# )"
      ],
      "metadata": {
        "id": "LBHbuuU0fGuh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ## Just searching for assets over Panama in order to\n",
        "# bbox = [-80.11, 8.71, -79.24, 9.38]\n",
        "# search = catalog.search(\n",
        "#     collections=[\"sentinel-1-rtc\"], bbox=bbox, datetime=\"2022-05-02/2022-05-09\"\n",
        "# )\n",
        "# items = search.item_collection()\n",
        "# print(f\"Found {len(items)} items\")\n",
        "# item = items[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dbKV8kGsfxb4",
        "outputId": "67d6638b-9d1e-43a3-e983-851653b87719"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 3 items\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Image(url=item.assets[\"rendered_preview\"].href)"
      ],
      "metadata": {
        "id": "EFKTrc8Vh08R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# table = rich.table.Table(\"key\", \"value\")\n",
        "# for k, v in sorted(item.properties.items()):\n",
        "#     table.add_row(k, str(v))\n",
        "\n",
        "# table"
      ],
      "metadata": {
        "id": "XCcEBqq8iAHp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# time_slice = \"2020-03-20/2020-03-21\"\n",
        "time_slice = '2020-03-20/2021-03-21'\n",
        "assests = ['vh','vv']\n",
        "vh_vv = []\n",
        "for coordinates in tqdm(crop_presence_data['Latitude and Longitude']):\n",
        "    vh_vv.append(get_sentinel_data(coordinates,time_slice,assests))\n",
        "vh_vv_data = pd.DataFrame(vh_vv,columns =['vh','vv'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6_bzGpwlHg3x",
        "outputId": "6085107e-f19f-4928-8457-162445f54cb2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 600/600 [08:14<00:00,  1.21it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# latlong = '(10.323727047081501, 105.2516346045924)'\n",
        "# latlong=latlong.replace('(','').replace(')','').replace(' ','').split(',')\n",
        "# bbox_of_interest = [float(latlong[1]) , float(latlong[0]), float(latlong[1])+100.00 , float(latlong[0])]\n",
        "# bbox_of_interest"
      ],
      "metadata": {
        "id": "8TzdxYe1TuZ8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def combine_two_datasets(dataset1,dataset2):\n",
        "    '''\n",
        "    Returns a  vertically concatenated dataset.\n",
        "    Attributes:\n",
        "    dataset1 - Dataset 1 to be combined\n",
        "    dataset2 - Dataset 2 to be combined\n",
        "    '''\n",
        "    data = pd.concat([dataset1,dataset2], axis=1)\n",
        "    return data"
      ],
      "metadata": {
        "id": "wUEqxXVMH9o7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "crop_data = combine_two_datasets(crop_presence_data,vh_vv_data)\n",
        "crop_data.head()"
      ],
      "metadata": {
        "id": "an57RFrkI1fP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "672823ed-840b-4384-a345-7dbd5742ee3f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     Latitude and Longitude Class of Land        vh        vv\n",
              "0   (10.323727047081501, 105.2516346045924)          Rice  0.028296  0.112560\n",
              "1  (10.322364360592521, 105.27843410554115)          Rice  0.051894  0.048721\n",
              "2  (10.321455902933202, 105.25254306225168)          Rice  0.033304  0.081855\n",
              "3  (10.324181275911162, 105.25118037576274)          Rice  0.010928  0.024132\n",
              "4  (10.324635504740822, 105.27389181724476)          Rice  0.012968  0.015349"
            ],
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              "\n",
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              "      <div>\n",
              "<style scoped>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Latitude and Longitude</th>\n",
              "      <th>Class of Land</th>\n",
              "      <th>vh</th>\n",
              "      <th>vv</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(10.323727047081501, 105.2516346045924)</td>\n",
              "      <td>Rice</td>\n",
              "      <td>0.028296</td>\n",
              "      <td>0.112560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(10.322364360592521, 105.27843410554115)</td>\n",
              "      <td>Rice</td>\n",
              "      <td>0.051894</td>\n",
              "      <td>0.048721</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(10.321455902933202, 105.25254306225168)</td>\n",
              "      <td>Rice</td>\n",
              "      <td>0.033304</td>\n",
              "      <td>0.081855</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(10.324181275911162, 105.25118037576274)</td>\n",
              "      <td>Rice</td>\n",
              "      <td>0.010928</td>\n",
              "      <td>0.024132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(10.324635504740822, 105.27389181724476)</td>\n",
              "      <td>Rice</td>\n",
              "      <td>0.012968</td>\n",
              "      <td>0.015349</td>\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "crop_data.shape"
      ],
      "metadata": {
        "id": "BeI33Ozd67ix",
        "outputId": "4bbff02c-3111-4def-b32d-55d36cda8b98",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(600, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### Radar Vegetation Index (RVI)\n",
        "#mean = d.mean(dim=['latitude','longitude']).compute()"
      ],
      "metadata": {
        "id": "mZkkgRz-xEui"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# crop_data.to_csv('/content/drive/MyDrive/crop_data.csv')"
      ],
      "metadata": {
        "id": "Kdcrv6w4ciRz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Building"
      ],
      "metadata": {
        "id": "RnQDL78AI6Yu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import RobustScaler\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.decomposition import PCA\n",
        "crop_data = crop_data[['vh','vv','Class of Land']]\n",
        "\n",
        "X = crop_data.drop(columns=['Class of Land']).values\n",
        "y = crop_data ['Class of Land'].values\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,stratify=y,random_state=40)\n",
        "\n",
        "# poly = PolynomialFeatures(degree = 3, interaction_only=True)\n",
        "# X_train = poly.fit_transform(X_train)\n",
        "# X_test = poly.fit_transform(X_test)\n",
        "\n",
        "sc = RobustScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)\n"
      ],
      "metadata": {
        "id": "_tF3k7bgI4dO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ## Explore MINMAX Scaler\n",
        "# ## Max Absolute Scaling\n",
        "# ## Robust Scaling\n",
        "# from sklearn.preprocessing import MinMaxScaler\n",
        "# sc = MinMaxScaler()\n",
        "# X_train = sc.fit_transform(X_train)\n",
        "# X_test = sc.transform(X_test)\n",
        "\n"
      ],
      "metadata": {
        "id": "pFqYLa-hJGYr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training"
      ],
      "metadata": {
        "id": "I6XpRdd4JZjX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier\n",
        "model = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1, max_depth=1, random_state=0)\n",
        "model.fit(X_train,y_train)"
      ],
      "metadata": {
        "id": "PZXq3l2cJbHW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "outputId": "9ac5d0cc-2271-4455-bb1d-7314655b80ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GradientBoostingClassifier(max_depth=1, random_state=0)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier(max_depth=1, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier(max_depth=1, random_state=0)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "insample_predictions = model.predict(X_train)\n",
        "print(\"Insample Accuracy {0:.2f}%\".format(100*accuracy_score(insample_predictions,y_train)))\n",
        "print(classification_report(insample_predictions,y_train))"
      ],
      "metadata": {
        "id": "qM4Flx87JlPD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6be54cac-e697-4570-e5a9-8b7c34d7e51b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Insample Accuracy 81.43%\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.84      0.80      0.82       222\n",
            "        Rice       0.79      0.83      0.81       198\n",
            "\n",
            "    accuracy                           0.81       420\n",
            "   macro avg       0.81      0.82      0.81       420\n",
            "weighted avg       0.82      0.81      0.81       420\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#  ## Trying out Gaussian Naive Bayes\n",
        "# from sklearn.naive_bayes import GaussianNB\n",
        "# gnb = GaussianNB()\n",
        "# y_pred = gnb.fit(X_train, y_train).predict(X_test)\n",
        "# print(\"Number of mislabeled points out of a total %d points : %d\" % (X_test.shape[0], (y_test != y_pred).sum()))\n",
        "# accuracy = (X_test.shape[0] - (y_test != y_pred).sum())/X_test.shape[0]\n",
        "# print(\"Insample Accuracy {0:.2f}%\".format(100*accuracy))\n",
        "# # print(classification_report(insample_predictions,y_train))"
      ],
      "metadata": {
        "id": "YHKyospuowTr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Nja37LbTrEje"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_confusion_matrix(true_value,predicted_value,title,labels):\n",
        "    '''\n",
        "    Plots a confusion matrix.\n",
        "    Attributes:\n",
        "    true_value - The ground truth value for comparision.\n",
        "    predicted_value - The values predicted by the model.\n",
        "    title - Title of the plot.\n",
        "    labels - The x and y labels of the plot.\n",
        "    '''\n",
        "    cm = confusion_matrix(true_value,predicted_value)\n",
        "    ax= plt.subplot()\n",
        "    sns.heatmap(cm, annot=True, fmt='g', ax=ax, cmap='Blues');\n",
        "    ax.set_xlabel('Predicted labels');\n",
        "    ax.set_ylabel('True labels');\n",
        "    ax.set_title(title);\n",
        "    ax.xaxis.set_ticklabels(labels);\n",
        "    ax.yaxis.set_ticklabels(labels);"
      ],
      "metadata": {
        "id": "Vywc-OLOKgon"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_confusion_matrix(y_train,insample_predictions,\"Model Level 1: Gradient Boosting Method In-Sample Results\",['Rice', 'Non Rice'])"
      ],
      "metadata": {
        "id": "Msbh8dJzKkmb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "6c9955d4-58d5-4ad6-d756-bf6d9642fdb5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Outsample Evaluation"
      ],
      "metadata": {
        "id": "i1FowOe8KnuU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "outsample_predictions = model.predict(X_test)\n",
        "print(\"Accuracy {0:.2f}%\".format(100*accuracy_score(outsample_predictions, y_test)))\n",
        "print(classification_report(y_test, outsample_predictions))"
      ],
      "metadata": {
        "id": "gRk5p5bGKpjg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6b88fe1d-6867-4adb-ab2c-cc6e1af29809"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy 88.89%\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.88      0.90      0.89        90\n",
            "        Rice       0.90      0.88      0.89        90\n",
            "\n",
            "    accuracy                           0.89       180\n",
            "   macro avg       0.89      0.89      0.89       180\n",
            "weighted avg       0.89      0.89      0.89       180\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_confusion_matrix(y_test, outsample_predictions,\"Model Level 1: Gradient Boosting Method Out-Sample Results\",['Rice', 'Non Rice'])"
      ],
      "metadata": {
        "id": "G-13ZK-EK0Yi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "23737a70-4c34-4a16-dad0-78bca63bed7b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_file = pd.read_csv('/content/drive/MyDrive/challenge_1_submission_template.csv')\n",
        "test_file.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "yMVnv5Y93G1L",
        "outputId": "cc8913a7-6443-4810-ea1f-1e647bfe0d4f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     Latitude and Longitude\n",
              "0   (10.18019073690894, 105.32022315786804)\n",
              "1  (10.561107033461816, 105.12772097986661)\n",
              "2  (10.623790611954897, 105.13771401411867)\n",
              "3  (10.583364246115156, 105.23946127195805)\n",
              "4   (10.20744446668854, 105.26844107128906)"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5e10ec10-0122-4afc-9bc7-4e208c067b9d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Latitude and Longitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(10.18019073690894, 105.32022315786804)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(10.561107033461816, 105.12772097986661)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(10.623790611954897, 105.13771401411867)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(10.583364246115156, 105.23946127195805)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(10.20744446668854, 105.26844107128906)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5e10ec10-0122-4afc-9bc7-4e208c067b9d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5e10ec10-0122-4afc-9bc7-4e208c067b9d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5e10ec10-0122-4afc-9bc7-4e208c067b9d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.utils import all_estimators\n",
        "def getClassifiers(classifier):\n",
        "    classifierModels = []\n",
        "    for name, ClassifierClass in all_estimators(type_filter=classifier):\n",
        "        try:\n",
        "            classifier = ClassifierClass()\n",
        "            classifierModels.append((name, classifier))\n",
        "        except Exception as e:\n",
        "            pass\n",
        "    return classifierModels\n",
        "\n",
        "classifierModels = getClassifiers('classifier')\n",
        "print(len(classifierModels))\n",
        "model1 = classifierModels[: 10]\n",
        "model2 = classifierModels[10: 20]\n",
        "model3 = classifierModels[20: 30]\n",
        "model4 = classifierModels[30: 34]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xE6nIt8KX7ij",
        "outputId": "cce5a954-872e-42a4-b481-dd214bc80d58"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "34\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## SELECTING DIFFERENT MODELS"
      ],
      "metadata": {
        "id": "YbRh0F0GJN_F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, r2_score\n",
        "\n",
        "def evaluate(model, XTrain, yTrain, XTest, yTest):\n",
        "    model.fit(XTrain, yTrain)\n",
        "    yPred = model.predict(XTest)\n",
        "    accuracy = accuracy_score(yTest, yPred)\n",
        "    # f1 = f1_score(yTest, yPred)\n",
        "    print(f'Model Name = {type(model).__name__}, accuracy score = {accuracy}')\n",
        "    print(classification_report(y_test, yPred))"
      ],
      "metadata": {
        "id": "_oc6PoiMYFzn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def tryModels(models):\n",
        "    for model in models:\n",
        "        try:\n",
        "            print(f'Running {type(model[1]).__name__}')\n",
        "            ans = evaluate(model[1], X_train, y_train, X_test, y_test)\n",
        "            # print(classification_report(y_test, outsample_predictions))\n",
        "            print(ans)\n",
        "        except Exception as e:\n",
        "            pass"
      ],
      "metadata": {
        "id": "p9GpjAqzYJHm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tryModels(model1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fo-Aa-6kYMGK",
        "outputId": "d4911e2d-4f2c-4608-c4ae-c25d1afd7d7e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Running AdaBoostClassifier\n",
            "Model Name = AdaBoostClassifier, accuracy score = 0.8166666666666667\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.80      0.84      0.82        90\n",
            "        Rice       0.84      0.79      0.81        90\n",
            "\n",
            "    accuracy                           0.82       180\n",
            "   macro avg       0.82      0.82      0.82       180\n",
            "weighted avg       0.82      0.82      0.82       180\n",
            "\n",
            "None\n",
            "Running BaggingClassifier\n",
            "Model Name = BaggingClassifier, accuracy score = 0.8444444444444444\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.82      0.89      0.85        90\n",
            "        Rice       0.88      0.80      0.84        90\n",
            "\n",
            "    accuracy                           0.84       180\n",
            "   macro avg       0.85      0.84      0.84       180\n",
            "weighted avg       0.85      0.84      0.84       180\n",
            "\n",
            "None\n",
            "Running BernoulliNB\n",
            "Model Name = BernoulliNB, accuracy score = 0.6722222222222223\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.70      0.61      0.65        90\n",
            "        Rice       0.65      0.73      0.69        90\n",
            "\n",
            "    accuracy                           0.67       180\n",
            "   macro avg       0.67      0.67      0.67       180\n",
            "weighted avg       0.67      0.67      0.67       180\n",
            "\n",
            "None\n",
            "Running CalibratedClassifierCV\n",
            "Model Name = CalibratedClassifierCV, accuracy score = 0.6444444444444445\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.69      0.53      0.60        90\n",
            "        Rice       0.62      0.76      0.68        90\n",
            "\n",
            "    accuracy                           0.64       180\n",
            "   macro avg       0.65      0.64      0.64       180\n",
            "weighted avg       0.65      0.64      0.64       180\n",
            "\n",
            "None\n",
            "Running CategoricalNB\n",
            "Running ComplementNB\n",
            "Running DecisionTreeClassifier\n",
            "Model Name = DecisionTreeClassifier, accuracy score = 0.7611111111111111\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.77      0.74      0.76        90\n",
            "        Rice       0.75      0.78      0.77        90\n",
            "\n",
            "    accuracy                           0.76       180\n",
            "   macro avg       0.76      0.76      0.76       180\n",
            "weighted avg       0.76      0.76      0.76       180\n",
            "\n",
            "None\n",
            "Running DummyClassifier\n",
            "Model Name = DummyClassifier, accuracy score = 0.5\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.50      1.00      0.67        90\n",
            "        Rice       0.00      0.00      0.00        90\n",
            "\n",
            "    accuracy                           0.50       180\n",
            "   macro avg       0.25      0.50      0.33       180\n",
            "weighted avg       0.25      0.50      0.33       180\n",
            "\n",
            "None\n",
            "Running ExtraTreeClassifier\n",
            "Model Name = ExtraTreeClassifier, accuracy score = 0.75\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.75      0.74      0.75        90\n",
            "        Rice       0.75      0.76      0.75        90\n",
            "\n",
            "    accuracy                           0.75       180\n",
            "   macro avg       0.75      0.75      0.75       180\n",
            "weighted avg       0.75      0.75      0.75       180\n",
            "\n",
            "None\n",
            "Running ExtraTreesClassifier\n",
            "Model Name = ExtraTreesClassifier, accuracy score = 0.8666666666666667\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.85      0.89      0.87        90\n",
            "        Rice       0.88      0.84      0.86        90\n",
            "\n",
            "    accuracy                           0.87       180\n",
            "   macro avg       0.87      0.87      0.87       180\n",
            "weighted avg       0.87      0.87      0.87       180\n",
            "\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tryModels(model2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xO_BshBrYOp9",
        "outputId": "43cb76b1-251c-4e14-e9f5-91b00d6ef924"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Running GaussianNB\n",
            "Model Name = GaussianNB, accuracy score = 0.5\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.00      0.00      0.00        90\n",
            "        Rice       0.50      1.00      0.67        90\n",
            "\n",
            "    accuracy                           0.50       180\n",
            "   macro avg       0.25      0.50      0.33       180\n",
            "weighted avg       0.25      0.50      0.33       180\n",
            "\n",
            "None\n",
            "Running GaussianProcessClassifier\n",
            "Model Name = GaussianProcessClassifier, accuracy score = 0.9\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.91      0.89      0.90        90\n",
            "        Rice       0.89      0.91      0.90        90\n",
            "\n",
            "    accuracy                           0.90       180\n",
            "   macro avg       0.90      0.90      0.90       180\n",
            "weighted avg       0.90      0.90      0.90       180\n",
            "\n",
            "None\n",
            "Running GradientBoostingClassifier\n",
            "Model Name = GradientBoostingClassifier, accuracy score = 0.85\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.84      0.87      0.85        90\n",
            "        Rice       0.86      0.83      0.85        90\n",
            "\n",
            "    accuracy                           0.85       180\n",
            "   macro avg       0.85      0.85      0.85       180\n",
            "weighted avg       0.85      0.85      0.85       180\n",
            "\n",
            "None\n",
            "Running HistGradientBoostingClassifier\n",
            "Model Name = HistGradientBoostingClassifier, accuracy score = 0.8388888888888889\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.81      0.89      0.85        90\n",
            "        Rice       0.88      0.79      0.83        90\n",
            "\n",
            "    accuracy                           0.84       180\n",
            "   macro avg       0.84      0.84      0.84       180\n",
            "weighted avg       0.84      0.84      0.84       180\n",
            "\n",
            "None\n",
            "Running KNeighborsClassifier\n",
            "Model Name = KNeighborsClassifier, accuracy score = 0.8777777777777778\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.85      0.91      0.88        90\n",
            "        Rice       0.90      0.84      0.87        90\n",
            "\n",
            "    accuracy                           0.88       180\n",
            "   macro avg       0.88      0.88      0.88       180\n",
            "weighted avg       0.88      0.88      0.88       180\n",
            "\n",
            "None\n",
            "Running LabelPropagation\n",
            "Model Name = LabelPropagation, accuracy score = 0.8611111111111112\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.85      0.88      0.86        90\n",
            "        Rice       0.87      0.84      0.86        90\n",
            "\n",
            "    accuracy                           0.86       180\n",
            "   macro avg       0.86      0.86      0.86       180\n",
            "weighted avg       0.86      0.86      0.86       180\n",
            "\n",
            "None\n",
            "Running LabelSpreading\n",
            "Model Name = LabelSpreading, accuracy score = 0.8555555555555555\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.84      0.88      0.86        90\n",
            "        Rice       0.87      0.83      0.85        90\n",
            "\n",
            "    accuracy                           0.86       180\n",
            "   macro avg       0.86      0.86      0.86       180\n",
            "weighted avg       0.86      0.86      0.86       180\n",
            "\n",
            "None\n",
            "Running LinearDiscriminantAnalysis\n",
            "Model Name = LinearDiscriminantAnalysis, accuracy score = 0.5\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.00      0.00      0.00        90\n",
            "        Rice       0.50      1.00      0.67        90\n",
            "\n",
            "    accuracy                           0.50       180\n",
            "   macro avg       0.25      0.50      0.33       180\n",
            "weighted avg       0.25      0.50      0.33       180\n",
            "\n",
            "None\n",
            "Running LinearSVC\n",
            "Model Name = LinearSVC, accuracy score = 0.6222222222222222\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.67      0.49      0.56        90\n",
            "        Rice       0.60      0.76      0.67        90\n",
            "\n",
            "    accuracy                           0.62       180\n",
            "   macro avg       0.63      0.62      0.62       180\n",
            "weighted avg       0.63      0.62      0.62       180\n",
            "\n",
            "None\n",
            "Running LogisticRegression\n",
            "Model Name = LogisticRegression, accuracy score = 0.6111111111111112\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.65      0.49      0.56        90\n",
            "        Rice       0.59      0.73      0.65        90\n",
            "\n",
            "    accuracy                           0.61       180\n",
            "   macro avg       0.62      0.61      0.61       180\n",
            "weighted avg       0.62      0.61      0.61       180\n",
            "\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tryModels(model3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xkJFpXHjYU7F",
        "outputId": "1f6aebbd-bba6-41f1-8c11-c533af122a57"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Running LogisticRegressionCV\n",
            "Model Name = LogisticRegressionCV, accuracy score = 0.6111111111111112\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.65      0.49      0.56        90\n",
            "        Rice       0.59      0.73      0.65        90\n",
            "\n",
            "    accuracy                           0.61       180\n",
            "   macro avg       0.62      0.61      0.61       180\n",
            "weighted avg       0.62      0.61      0.61       180\n",
            "\n",
            "None\n",
            "Running MLPClassifier\n",
            "Model Name = MLPClassifier, accuracy score = 0.7277777777777777\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.90      0.51      0.65        90\n",
            "        Rice       0.66      0.94      0.78        90\n",
            "\n",
            "    accuracy                           0.73       180\n",
            "   macro avg       0.78      0.73      0.71       180\n",
            "weighted avg       0.78      0.73      0.71       180\n",
            "\n",
            "None\n",
            "Running MultinomialNB\n",
            "Running NearestCentroid\n",
            "Model Name = NearestCentroid, accuracy score = 0.5\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.00      0.00      0.00        90\n",
            "        Rice       0.50      1.00      0.67        90\n",
            "\n",
            "    accuracy                           0.50       180\n",
            "   macro avg       0.25      0.50      0.33       180\n",
            "weighted avg       0.25      0.50      0.33       180\n",
            "\n",
            "None\n",
            "Running NuSVC\n",
            "Running PassiveAggressiveClassifier\n",
            "Model Name = PassiveAggressiveClassifier, accuracy score = 0.5444444444444444\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       1.00      0.09      0.16        90\n",
            "        Rice       0.52      1.00      0.69        90\n",
            "\n",
            "    accuracy                           0.54       180\n",
            "   macro avg       0.76      0.54      0.43       180\n",
            "weighted avg       0.76      0.54      0.43       180\n",
            "\n",
            "None\n",
            "Running Perceptron\n",
            "Model Name = Perceptron, accuracy score = 0.34444444444444444\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.36      0.39      0.37        90\n",
            "        Rice       0.33      0.30      0.31        90\n",
            "\n",
            "    accuracy                           0.34       180\n",
            "   macro avg       0.34      0.34      0.34       180\n",
            "weighted avg       0.34      0.34      0.34       180\n",
            "\n",
            "None\n",
            "Running QuadraticDiscriminantAnalysis\n",
            "Model Name = QuadraticDiscriminantAnalysis, accuracy score = 0.5166666666666667\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       1.00      0.03      0.06        90\n",
            "        Rice       0.51      1.00      0.67        90\n",
            "\n",
            "    accuracy                           0.52       180\n",
            "   macro avg       0.75      0.52      0.37       180\n",
            "weighted avg       0.75      0.52      0.37       180\n",
            "\n",
            "None\n",
            "Running RadiusNeighborsClassifier\n",
            "Running RandomForestClassifier\n",
            "Model Name = RandomForestClassifier, accuracy score = 0.8666666666666667\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.85      0.89      0.87        90\n",
            "        Rice       0.88      0.84      0.86        90\n",
            "\n",
            "    accuracy                           0.87       180\n",
            "   macro avg       0.87      0.87      0.87       180\n",
            "weighted avg       0.87      0.87      0.87       180\n",
            "\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tryModels(model4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jKyG8Jh8YcHz",
        "outputId": "4d9d2444-d976-4534-9fd5-fbdd7b1c8fc3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Running RidgeClassifier\n",
            "Model Name = RidgeClassifier, accuracy score = 0.6111111111111112\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.65      0.49      0.56        90\n",
            "        Rice       0.59      0.73      0.65        90\n",
            "\n",
            "    accuracy                           0.61       180\n",
            "   macro avg       0.62      0.61      0.61       180\n",
            "weighted avg       0.62      0.61      0.61       180\n",
            "\n",
            "None\n",
            "Running RidgeClassifierCV\n",
            "Model Name = RidgeClassifierCV, accuracy score = 0.6111111111111112\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.65      0.49      0.56        90\n",
            "        Rice       0.59      0.73      0.65        90\n",
            "\n",
            "    accuracy                           0.61       180\n",
            "   macro avg       0.62      0.61      0.61       180\n",
            "weighted avg       0.62      0.61      0.61       180\n",
            "\n",
            "None\n",
            "Running SGDClassifier\n",
            "Model Name = SGDClassifier, accuracy score = 0.34444444444444444\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.36      0.39      0.37        90\n",
            "        Rice       0.33      0.30      0.31        90\n",
            "\n",
            "    accuracy                           0.34       180\n",
            "   macro avg       0.34      0.34      0.34       180\n",
            "weighted avg       0.34      0.34      0.34       180\n",
            "\n",
            "None\n",
            "Running SVC\n",
            "Model Name = SVC, accuracy score = 0.5\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.00      0.00      0.00        90\n",
            "        Rice       0.50      1.00      0.67        90\n",
            "\n",
            "    accuracy                           0.50       180\n",
            "   macro avg       0.25      0.50      0.33       180\n",
            "weighted avg       0.25      0.50      0.33       180\n",
            "\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.semi_supervised import LabelPropagation\n",
        "model0 = LabelPropagation(kernel='knn', n_neighbors=7, max_iter=1000)\n",
        "model0.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "MSPQbAX9YfxX",
        "outputId": "875cfdf5-83ff-4b45-a41f-21ec5b008db3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LabelPropagation(kernel='knn')"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LabelPropagation(kernel=&#x27;knn&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LabelPropagation</label><div class=\"sk-toggleable__content\"><pre>LabelPropagation(kernel=&#x27;knn&#x27;)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Grid Search method for Hyperparameter tuning\n",
        "# from sklearn.model_selection import GridSearchCV\n",
        "# lp = LabelPropagation(kernel='knn',max_iter=1000)\n",
        "# params = {\n",
        "#     'n_neighbors' : [3,5,7,9]\n",
        "# }\n",
        "# grid = GridSearchCV(lp,params,cv=5,scoring='accuracy')\n",
        "# grid.fit(X_train, y_train)\n",
        "# model = grid.best_estimator_\n",
        "# predicted_labels1 = model.predict(X_test)\n",
        "# print(\"Outsample Accuracy {0:.2f}%\".format(100*accuracy_score(predicted_labels1,y_test)))\n",
        "# print(classification_report(predicted_labels1,y_test))\n",
        "# grid.best_params_\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qsKrwUlZpcWV",
        "outputId": "e79dc363-f129-4cc5-ece9-2ad79020d861"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Outsample Accuracy 87.78%\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.91      0.85      0.88        96\n",
            "        Rice       0.84      0.90      0.87        84\n",
            "\n",
            "    accuracy                           0.88       180\n",
            "   macro avg       0.88      0.88      0.88       180\n",
            "weighted avg       0.88      0.88      0.88       180\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'n_neighbors': 5}"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "insample_predictions0 = model0.predict(X_test)\n",
        "print(\"Outsample Accuracy {0:.2f}%\".format(100*accuracy_score(insample_predictions0,y_test)))\n",
        "print(classification_report(insample_predictions0,y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "487d9QQCZCQh",
        "outputId": "86de3fd6-5508-43a0-ce20-6491c8f59343"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Outsample Accuracy 88.33%\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.92      0.86      0.89        97\n",
            "        Rice       0.84      0.92      0.88        83\n",
            "\n",
            "    accuracy                           0.88       180\n",
            "   macro avg       0.88      0.89      0.88       180\n",
            "weighted avg       0.89      0.88      0.88       180\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "rf = RandomForestClassifier()\n",
        "params = {\n",
        "    'n_estimators' : [100,150,200,250],\n",
        "    'criterion' : ['gini','entropy','log_loss'],\n",
        "    'max_depth' : [1,2,3,4,5,6,7]\n",
        "}\n",
        "grid = GridSearchCV(rf,params,scoring='accuracy',cv=3)\n",
        "grid.fit(X_train,y_train)\n",
        "rf_model = grid.best_estimator_\n",
        "predicted_labels2 = rf_model.predict(X_test)\n",
        "print(\"Outsample Accuracy {0:.2f}%\".format(100*accuracy_score(predicted_labels2,y_test)))\n",
        "print(classification_report(predicted_labels2,y_test))\n",
        "grid.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GZnSQ5H4ZORe",
        "outputId": "5b3fbb20-9136-4442-a796-2db08d42eb7e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Outsample Accuracy 86.67%\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.89      0.85      0.87        94\n",
            "        Rice       0.84      0.88      0.86        86\n",
            "\n",
            "    accuracy                           0.87       180\n",
            "   macro avg       0.87      0.87      0.87       180\n",
            "weighted avg       0.87      0.87      0.87       180\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'criterion': 'gini', 'max_depth': 4, 'n_estimators': 100}"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "knn_model = KNeighborsClassifier()\n",
        "# knn_model.fit(X_train, y_train)\n",
        "# predicted_labels3 = knn_model.predict(X_test)\n",
        "# print(\"Outsample Accuracy {0:.2f}%\".format(100*accuracy_score(predicted_labels3,y_test)))\n",
        "# print(classification_report(predicted_labels3,y_test))"
      ],
      "metadata": {
        "id": "-XVUWd_CZX7J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "params = {\n",
        "    'n_neighbors' : [5,6,7],\n",
        "    'weights' : ['uniform','distance'],\n",
        "    'algorithm' : ['auto','ball_tree','kd_tree','brute'],\n",
        "    'p' : [1,2]\n",
        "}\n",
        "grid = GridSearchCV(knn_model,params,cv=3,scoring='accuracy')\n",
        "grid.fit(X_train, y_train)\n",
        "model = grid.best_estimator_\n",
        "predicted_labels3 = model.predict(X_test)\n",
        "print(\"Outsample Accuracy {0:.2f}%\".format(100*accuracy_score(predicted_labels3,y_test)))\n",
        "print(classification_report(predicted_labels3,y_test))\n",
        "grid.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3MDBDg1WiXGF",
        "outputId": "b1568a02-5a32-4be8-e3a2-504a015f4ce8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Outsample Accuracy 87.78%\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.91      0.85      0.88        96\n",
            "        Rice       0.84      0.90      0.87        84\n",
            "\n",
            "    accuracy                           0.88       180\n",
            "   macro avg       0.88      0.88      0.88       180\n",
            "weighted avg       0.88      0.88      0.88       180\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'algorithm': 'auto', 'n_neighbors': 5, 'p': 2, 'weights': 'uniform'}"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## BEST MODEL - HYPERPARAMETER TUNING"
      ],
      "metadata": {
        "id": "IhKUX9hgJW8S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.gaussian_process import GaussianProcessClassifier\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.gaussian_process.kernels import RBF\n",
        "from sklearn.gaussian_process.kernels import DotProduct\n",
        "from sklearn.gaussian_process.kernels import Matern\n",
        "from sklearn.gaussian_process.kernels import RationalQuadratic\n",
        "from sklearn.gaussian_process.kernels import WhiteKernel\n",
        "gpc = GaussianProcessClassifier()\n",
        "# param_grid = {\n",
        "#     'kernel' : [1*RBF(), 1*DotProduct(), 1*Matern(), 1*RationalQuadratic(), 1*WhiteKernel()]\n",
        "# }\n",
        "# grid = GridSearchCV(gpc, param_grid, scoring='accuracy', cv=5, n_jobs=-1)\n",
        "param_grid = {\n",
        "    'max_iter_predict' : [100,150,200],\n",
        "    'n_restarts_optimizer' : [1,2],\n",
        "    'multi_class' : ['one_vs_rest', 'one_vs_one']\n",
        "}\n",
        "grid = GridSearchCV(gpc, param_grid, scoring='accuracy', cv=5)\n",
        "grid.fit(X_train, y_train)\n",
        "model = grid.best_estimator_\n",
        "predicted_labels4 = model.predict(X_test)\n",
        "print(\"Outsample Accuracy {0:.2f}%\".format(100*accuracy_score(predicted_labels4,y_test)))\n",
        "print(classification_report(predicted_labels4,y_test))\n",
        "# model = grid.best_estimator_\n",
        "# predicted_labels = model.predict(X_test)\n",
        "# print(\"Outsample Accuracy {0:.2f}%\".format(100*accuracy_score(predicted_labels,y_test)))\n",
        "# print(classification_report(predicted_labels,y_test))\n",
        "# print('Best Mean Accuracy: %.3f' % grid.best_score_)\n",
        "grid.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zWaFdE7uZyoW",
        "outputId": "448b3643-a5f9-4688-ba64-c2252f4e156c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Outsample Accuracy 90.00%\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.89      0.91      0.90        88\n",
            "        Rice       0.91      0.89      0.90        92\n",
            "\n",
            "    accuracy                           0.90       180\n",
            "   macro avg       0.90      0.90      0.90       180\n",
            "weighted avg       0.90      0.90      0.90       180\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'max_iter_predict': 100,\n",
              " 'multi_class': 'one_vs_rest',\n",
              " 'n_restarts_optimizer': 1}"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    }
  ]
}